System and method for identifying and analyzing personal context of a user

ABSTRACT

A method and system for identifying personal context of a user having a portable mobile communication device at a particular location for deriving social interaction information of the user, wherein the user within a predefined range is identified using personal context of the user at the particular location and the identified personal context of the user is assigned with the confidence value. Further the current location information of the user within the particular location is obtained by fusing assigned confidence value. Further the proximity of the user in the current location is estimated by finding the accurate straight line distance between users. Further the two users having similar current location information at the particular location are grouped together with the predefined density criteria. Finally the social interaction information of the user is derived by multimodal sensor data fusion at the fusion engine and represented using a human network graph.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application is a National Stage Entry under 35 U.S.C. §371 ofInternational Application No. PCT/IN2013/000045, filed on Jan. 22, 2013,which claims priority from Indian Patent Application No. 313/MUM/2012,filed on Feb. 2, 2012. The entire contents of the above-referencedapplications are expressly incorporated herein by reference for allpurposes.

FIELD OF THE INVENTION

This invention generally relates to the field of personal contextidentification of a person. More particularly, the invention relates toa system and method for personal context identification for derivingsocial interaction information of the person.

BACKGROUND OF THE INVENTION

Analysis of a person's behavior has been an important aspect that has aplurality of applications in the field of marketing, organizationaldevelopment etc. Due to this the field of personal context analysis isgaining wide importance. Specifically organizations employing a largenumber of employees are concerned to analyze the behavior of anindividual for faster and better growth of the organization. Theincreasing need of analysis in terms of personal context has led toincreasing growth in the field of Organizational Behavior Analysis,Workspace Ergonomics Analysis, Discovering the user's physicalinteraction network, User Studies Analysis, Market Study and Real-timeUsage capture etc.

A number of technologies are available in the market for analyzingsocial behavior of a person using the reality mining techniques andother related aspects such as work cultures which have dedicatedsoftware and hardware requirements of the system. Such systems analyzeOrganizational Behavior based on user context sensing via speciallydesigned devices that incorporate all required sensors. These devicesinteract with each other and a server to gather information associatedwith an individual. However, such systems and devices pose a threat asdata related to a number of individuals is required to be transmitted toa back-end server for further processing, thereby raising privacyconcerns. Moreover, further refinement and data processing at a distantlocated server leads to heavy transmission costs. Further, such datatransmission leads to extra usage of battery power in order to transmiteach and every particular sensed detail to the back-end server withoutprocessing it.

Furthermore, an additional device is required to be deployed at an extracost in order to track an individual's behavior. Such a device does notgenerally has an always-on connectivity to dump the user data collectedto the back-end server for further analysis—it needs to be docked to aspecial data collection station to transfer the data. Also, suchavailable devices do not have provision to connect to additionalexternal sensors over wireless, so the extensibility of the system tonewer applications is limited.

Also, current solutions for context recognition and analysis using thereality mining techniques are dependent on wearable sensors and mobiledevices for sensing a user's activity, location and proximity withrespect to other users. There are different algorithms used to arrive atthe conclusion of a user's attributes in the real world. Such resultsare often inaccurate due to errors in sensor readings and change inambient environment. Such inaccuracies can cause discrepancies andmalfunctioning in the case of ubiquitous applications. Furthermore, thesensors used are very limited in the kind of data they provide and mostof the time specialized sensors are needed for deployment.

As a result there is growing need to integrate a personal contextanalysis system with a more efficient, widely available anduser-friendly device which is easy to carry and simple to operate, andthereby eliminating the need for a separate special device or datacollection system. There is also a need to process the raw sensory dataat the sensing device itself to preserve battery life of the device(radio communication takes up most of the battery life), and therebyalso addressing the privacy preservation and data transmission costconcerns.

Moreover, a provision to connect to additional external sensors throughexisting communication means like USB, Wi-Fi or Bluetooth will also leadto better grasp of an individual's behavior. Further the personalcontext analysis will also lead to create a social network based on reallife fixations and affinities of the user.

OBJECTS OF THE INVENTION

The principle object of the present invention is to implement areal-time personal context identification system using reality miningtechniques by means of a widely available mobile communication device.

Another significant object of the invention is to use the existingsensing means present in a mobile communication device such as anon-board microphone-speaker combine, accelerometer, camera, etc,soft/virtual sensors like networking website profile of the user, emailheaders of the user, Rich Site Summary (RSS) feed and social blogprofile of the user, along with building management system (BMS) accesscontrol which works on real-time data brought into the phone usingvarious data communication methods to capture an individual's behavior.

Another significant object of the invention is to represent the socialnetwork of the user in the form of graphs depicting the socialinteraction information of the user's interaction while working in acubicle, interaction while leading a meeting, interaction of a presenterin a session, a passive listener in meeting, interaction during a groupdiscussion and the like.

Another object of the invention is to assign the confidence value to theexisting sensing means capturing the user's information.

Another object of the invention is to group users having similarlocation information.

Another object of the invention is to fuse the multimodal data fromvarious sources at the backend server.

Yet another object of the invention is to provide connectivity with oneor more external sensing devices for capturing additional detailsregarding the individual user.

Yet another object of the invention is to reduce battery consumption andtransmission cost of the system by pre-processing the sensor informationon the mobile communication device itself.

SUMMARY OF THE INVENTION

Before the present methods, systems, and hardware enablement aredescribed, it is to be understood that this invention in not limited tothe particular systems, and methodologies described, as there can bemultiple possible embodiments of the present invention which are notexpressly illustrated in the present disclosure. It is also to beunderstood that the terminology used in the description is for thepurpose of describing the particular versions or embodiments only, andis not intended to limit the scope of the present invention.

The present invention provides a method and system for identifyingpersonal context of at least one user having a portable mobilecommunication device at a particular location for deriving socialinteraction information of the user. The social interaction informationof the user may be the physical interaction between the two users.

In an embodiment of the present invention, a system and method forcapturing and processing multi-sensor data received from a mobilecommunication device for the purpose of personal context analysis isprovided. In an aspect, the mobile communication device may be a mobilephone, tablet or any such mobile device with adequate processing powerand suitably adapted with required sensors. The mobile communicationdevice may also be connected to one or more external sensors such asultrasound sensors, EEG sensors, and the like. A low-energy-consumingand low-sampling-rate data acquisition/capturing method is utilized forcapturing the sensory data. The sensing process will be aware of thedevice context such as battery level, memory usage etc towards anefficient and robust sensing strategy. Further, onboard analytics of thesensor data on the mobile communication device itself is utilized forextracting one or more parameters. For example, accelerometer analyticsfor activity detection, baseband communication using ultrasound forlocalization and proximity detection, microphone captured audioanalytics for emotion detection and neighborhood sensing, camera forambient lighting detection and optional analysis of externalphysiological sensors such as EEG sensor for further insights into usercontext. The system enables the sending of onboard analyzed parametersfrom the mobile communication device as a gateway to a back-end systemover a network connection such as the Internet. In an aspect, theonboard analyzed parameters may be encrypted for security/privacypurposes. In another aspect, the mobile device may collaborate andexchange information between one-another to get more contextinformation.

In an embodiment of the invention a method is provided for identifyingpersonal context of at least one user having a portable mobilecommunication device. The method includes determining a location of atleast one user within a predefined range by identifying a personalcontext of the user at the location, wherein the personal context of theuser is identified using at least an external sensor and an internalsensor embedded in the mobile communication device, assigning predefinedconfidence values to the identified personal context, obtaining currentlocation information of the user, grouping, based on predefined densitycriteria, at least two users having similar current location informationat the location of the user, estimating, using the external sensor andinternal sensor, a straight line distance between the at least twogrouped users, and deriving social interaction information of the userby fusing the current location information of the user, the estimatedstraight line distance, and data received from a web sensor.

All these parametric information collected from sensors may then befurther analyzed at the back-end server for creating individual andaggregated user context that can be used for Organizational BehaviorAnalysis, Workspace Ergonomics Analysis, Discovering the user's physicalinteraction network and also for Measuring and analyzing user responsein User Studies etc. The overall system is also beneficial for Learningthe user personal context in general and applying the knowledge tocreate adaptive intelligent systems that respond with action orinformation that is relevant to the user and for capturing and analyzinguser-specific real-time service consumption data. This information isstored in proper formats and will also enhance in population modelingand mass people behavioral modeling in a city while doing urban citymodeling.

In an embodiment of the invention a system is provided for the mobilecommunication device which further comprises an internal sensor, aprocessing module, an internal memory, a transmission module, and aswitching module. The internal sensor is adapted for sensing a personalcontext of the user and sending the personal context to a processingmodule of the mobile communication device, the processing module isadapted to perform on-board processing of sensory data received from anexternal sensor and the internal sensor, and the internal memory iscommunicatively coupled with the processing module and adapted to storethe processed sensory data. The transmission module is adapted totransmit the processed sensory data to a back end server hosting afusion engine and communicatively coupled to a database adapted to storefused confidence values, fused current location information of the user,and the personal context of the user and a switching module adapted forswitching between other mobile device applications of the internalsensor and the personal context of the user when an interrupt isgenerated from regular activities of the mobile communication device.Further, the external sensor is adapted for sensing the personal contextof the user and sending the personal context of the user to theprocessing module via an external sensor interface of the mobilecommunication device, and the back end server is adapted for fusingassigned predefined confidence values of the personal context of theuser, current location information of the user, and a derived accuratestraight line distance between at least two users in a group at alocation of the user. The system further comprises a fusion engine, afront end application and a database.

In another embodiment of the invention a system further comprises alocalization module; a confidence value assignment module; a currentlocation identification module; a proximity estimation module and agrouping module. The localization module is adapted for locating atleast one user within a predefined range by identifying the personalcontext of the user at the location, wherein the personal context of theuser is identified using the external sensor and the internal sensor.The confidence value assignment module is adapted for assigning thepredefined confidence values to the identified personal context of theuser. The current location identification module is adapted forobtaining current location information of the user within the locationby fusing the assigned confidence values using a fusion engine. Thegrouping module is adapted for grouping, using a predefined densitycriteria, at least two users having similar current location informationat the location. The proximity estimation module is adapted forestimating, using the external sensor and the internal sensor, anaccurate straight line distance between the grouped at least two users.The fusion engine is adapted for deriving the social interactioninformation of the user by fusing the current location information ofthe user and estimated accurate straight line distance.

In yet another of the invention, a non-transitory computer readablemedium storing machine readable instructions is disclosed. Theinstructions are executable by one or more processors for determining alocation of at least one user within a predefined range by identifying apersonal context of the user at the location, wherein the personalcontext of the user is identified using at least an external sensor andan internal sensor embedded in the mobile communication device,assigning predefined confidence values to the identified personalcontext, obtaining current location information relating to the user atthe location by fusing the assigned confidence values, grouping, basedon predefined density criteria, at least two users having similarcurrent location information at the location of the user, estimating,using the external sensor and internal sensor, a straight line distancebetween the at least two grouped users, and deriving social interactioninformation of the user by fusing the current location information ofthe user, the estimated straight line distance, and data received from aweb sensor.

The above said system, method, and computer-readable medium arepreferably for identifying personal context of at least one user havinga portable mobile communication device at a particular location forderiving social interaction information of the user but also may be usedfor many other applications.

BRIEF DESCRIPTION OF DRAWINGS

The foregoing summary, as well as the following detailed description ofpreferred embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating theinvention, there is shown in the drawings example constructions of theinvention; however, the invention is not limited to the specific systemand method disclosed in the drawings:

FIG. 1 illustrates components of a system for identifying personalcontext, in accordance with an embodiment.

FIG. 2 represents a block diagram for personal context based socialinteraction system, in accordance with an embodiment.

FIG. 3 illustrates a logical flow for identifying personal context, inaccordance with an embodiment.

FIG. 4 illustrates the logical flow for personal context based socialinteraction, in accordance with an embodiment.

FIG. 5 depicts the normalized histogram representing number of phonesbeing detected at the same location, in accordance with an exemplaryembodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Some embodiments of this invention, illustrating all its features, willnow be discussed in detail. The words “comprising,” “having,”“containing,” and “including,” and other forms thereof, are intended tobe equivalent in meaning and be open ended in that an item or itemsfollowing any one of these words is not meant to be an exhaustivelisting of such item or items, or meant to be limited to only the listeditem or items.

It must also be noted that as used herein and in the appended claims,the singular forms “a,” “an,” and “the” include plural references unlessthe context clearly dictates otherwise. Although any systems and methodssimilar or equivalent to those described herein can be used in thepractice or testing of embodiments of the present invention, thepreferred, systems and methods are now described.

One or more components of the invention are described as module for theunderstanding of the specification. For example, a module may includeself-contained component in a hardware circuit comprising of logicalgate, semiconductor device, integrated circuits or any other discretecomponent. The module may also be a part of any software programexecuted by any hardware entity for example processor. Theimplementation of module as a software program may include a set oflogical instructions to be executed by the processor or any otherhardware entity. Further a module may be incorporated with the set ofinstructions or a program by means of an interface.

The disclosed embodiments are merely exemplary of the invention, whichmay be embodied in various forms.

FIG. 1 refers to a system (100) for personal context identification inaccordance with an exemplary embodiment of the invention. The system(100) is constructed using one or more modules functioning independentlyor in combination to perform personal context analysis. The system (100)comprises of Mobile communication device (102) that further comprises ofinternal sensors (104) like accelerometer, camera, GPS, microphones andthe like for sensing user activities, location, audio, and ambientlight. The internal sensors further provide means for localization andproximity detection using ultrasonic and upper audio band. An externalsensor interface (108) connects external sensors (118) like wearable EEGhaving low-energy-consumption and low-sampling-rate with the mobilecommunication device (102). A processing module (110) and internalmemory (116) performs onboard analytics of the sensor data captured byvarious internal and external sensors. The mobile communication device(102) further comprises a transmission module (114) for transmittingrelevant information to the backend server (120) and a switching module(112) for switching between other mobile device applications (106) ofinternal sensors (104) and personal context analysis when an interruptis generated from regular activities of mobile communication device(102).

The processing module (110) processes the sensed data, separates thecontext data and transmits only the relevant information for personalcontext identification to the backend server (120) with the help oftransmission module (114). The backend server (120) processes andanalyzes the information received from the transmission module (114)present in the mobile communication device (102). The backend server(120) then sorts and processes each-and-every information related to aspecific user. This information is stored into the database (122) and isaccessed by the front end applications (124) through backend server(120) for creating individual and aggregated user context that can beused for Organizational Behavior Analysis, Workspace Ergonomics Analysisand also for Measuring and analyzing user response in User Studies etc.

The frontend application (124) through backend server (120) is furtheradapted for rendering the derived social interaction information of theuser in the form of statistic representation. The statisticrepresentation may include the multimodal social graph with edges havingmultiple attributes and can be queried along multiple dimensionson-demand. The social graph can be prepared by reading the attributesfrom two fold sensor input, the first being the analyzed output of thephysical sensors, and the second being the feed from web based softsensors. The analyzed data is used to generate the nodes and edges ofthe social graph.

FIG. 2 represents the block diagram for personal context based socialinteraction deriving system (200). The system comprises of alocalization module (202); a confidence value assignment module (204); acurrent location identification module (206); a proximity estimationmodule (208); a grouping module (210); and a fusion engine (212) hostedby the back end server (120).

According to an exemplary embodiment of the present invention, thesystem (200) is configured to derive personal context based socialinteraction. The personal context of the user comprises of the identityof the user i.e. who the person is, the proximity of the user i.e. thecloseness and the duration of the user to other individuals or users,the activities of the user i.e. working in cubicle, discussion room,corridor and the like, and the location of the user. The socialinteraction of the user is the physical interaction of the user withother individuals. The system (200) comprises of the localization module(202) adapted to locate the user within a predefined range of thevarious sources for identifying the personal context of the user at aparticular location. The personal context of the user is identifiedusing the external sensors (118) and the internal sensors (104) embeddedin the mobile communication device (102). Further the confidence valueassignment module (204) is used to assign the predefined confidencevalues to the identified personal context of the user depending on thesources from which the user is localized. Precise current locationinformation of the user inside the particular location is obtained usingthe current location identification module (206). The precise currentlocation information is obtained by fusing the assigned confidence valueof personal context of the user using a fusion engine (212). Further thesystem groups the two users located with the similar current locationinformation using the grouping module (208). The grouping of the twousers is decided based on certain with predefined density criteria. Theaccurate straight line distance between the two users in a group at theparticular location is derived using the proximity estimation module(210). The fusion engine (212) which is hosted by the back end server(120) is used to derive the social interaction information of the userby fusing the current location information of the user and derivedaccurate straight line distance between the two users in a group at theparticular location.

The external sensors (118) are selected from the group comprising ofaccess control entry/exit information from building management system(BMS), surveillance using depth camera like 3D camera, wearable EEG,ultrasound and the like. The internal sensors (104) are embedded in themobile communication device (102) are selected form the group comprisingof an accelerometer, a magnetometer, a speech recognizer, a gyroscopeand a W-Fi signature and the like.

The localization module (202) in which the indoor location detection iscaptured using access control logs of the buildings, Wi-Fi signatures,magnetometer signatures, accelerometer analytics on mobile phones andthe like. The Wi-Fi fingerprinting and/or triangulation based on RSSIcan also provide indoor localization. The accelerometer, magnetometerand gyroscope are combined to sense complex motion like walking orclimbing stairs or elevator or sitting/standing etc.

The personal context of the user is selected from the group comprisingof identity, proximity, activity and location. The identity of the maybe captured by the means of the user's mobile phone which is tagged tothe user, the user's Smart card while the user passes through BMS, theuser's skeletal structure and gait measurements taken using 3D depthcameras and the like. The proximity of the user may be captured by meansof the Bluetooth of the user, proximity sensors located in the buildingstructure, infrared proximity sensing and the like. The activity of theuser may be captured by the social interaction of the user with thepeople around which may comprises of information like duringpresentation, who all are listening? Who is asking questions? During agroup discussion and also gesture recognition may be used to identifyinteractions like handshakes and card exchanges etc. The location of theuser may be detect using the localization module (202).

The confidence value assignment module (204) assigns the predefinedconfidence value to the identified personal context of the user based onthe source of information. The predefined confidence value or theconfidence score of the internal source and external source shall varybetween 0% and 100% but may not inclusive of either bound. For example,the predefined confidence value of the building management system (BMS)data is 100%. The speech recognition software may provide a detectionscore which may be considered as the confidence value. The localizationrange from the speech data may be of the order of 10 sq. m. but notlimited to that. Similarly the case may be of Wi-Fi signature andaccelerometer data. The confidence scores may vary from the sources fromwhich they are captured, depending on how much data is captured and theenvironment from which the data is captured, the same sensor andanalytics might produce very high to very low confidence values.

In an embodiment of the present invention, the joint probabilityfunction which is used for finding the location information of anindividual user is obtained by the following equation:

$\begin{matrix}{L_{j}^{1} = {\prod\limits_{i = 1}^{4}\; {P_{ji}\left( {{loc}_{i}/x} \right)}}} & (1)\end{matrix}$

Where P_(ji)(loc_(i)/x) is the probability distribution function of thelocation of j^(th) individual given the observation from the i^(th)sensor data.

The P_(ji)(loc_(i)/x) is calculated from Bayes theorem, given below:

${P_{ji}\left( {{loc}/x} \right)} = \frac{{p\left( {x/{loc}} \right)}*{p({loc})}}{\sum\limits_{k}^{\;}\; {{p\left( {x/{loc}_{k}} \right)}*{p\left( {loc}_{k} \right)}}}$

Where p(loc) is the prior probability of the location of j. Thus thelocation of individual j is given by,

$\begin{matrix}{{loc}_{j}^{1} = {\underset{{loc}_{i} \in S}{\arg \; \max}\mspace{14mu} L_{j}^{1}}} & (2)\end{matrix}$

Where S is the physical space in which the individual j can belong. Theconfidence score associated with the observation of L¹ _(j) (loc¹ _(j))is S_(j) ¹.

In an embodiment of the present invention, the current locationidentification module (206) obtains precise current location informationof the user within the particular location.

In an embodiment of the present invention, the proximity estimationmodule (208) is used to estimate the proximity for deriving accuratestraight line distance between at least two users in a group at thecurrent location by utilizing the external sensors (118) and theinternal sensors (104) embedded in the mobile communication device (102)of the user.

In an embodiment of the present invention, the proximity estimationmodule (208) may use indoor proximity detection using Bluetooth, audioon mobile, 3D depth-camera, real life communications discovery byscanning the e-mail headers of the individual which provides informationlike To, CC and Subject fields giving an indication of the user'scommunication with the other people, and the like. The Bluetooth may beused to detect proximity since commercial phones come with class IIBluetooth which provides only short range. The 3D depth cameras provideaccurate measurement of distance and direction between people and henceprovide a very good source for detecting proximity. The Email files andarchives parsed are read using many commercially or open source emailtools including the Java mail API.

In an embodiment of the present invention, the mobile based proximitysensing is used as the pair-wise individual for different users of thegroup of people. From the “mobile proximity sensing”, distance d betweenthe individuals j and l, is given by the probability distributionfunction for the pair (j and i to be at distance d) as is given byP_(jlm)(d/x) The P_(jlm)(d/x) is calculated from Bayes theorem, asbelow:

${P_{jlm}\left( {d/x} \right)} = \frac{{p\left( {x/d} \right)}*{p(d)}}{\sum\limits_{i}^{\;}\; {{p\left( {x/d_{i}} \right)}*{p\left( d_{i} \right)}}}$

where p(d) is the prior probability of the distance between j and i.hence,

$\begin{matrix}{d_{jl}^{m} = {\underset{d \in S}{\arg \; \max}\mspace{14mu} {P_{jlm}\left( {d/x} \right)}}} & (3)\end{matrix}$

The confidence score S_(jl) ^(m) may be associated with the observationof is P_(jlm)(d_(jl) ^(m)/x).

For the distance d between the individuals j and i, using “3D cameradata” is given by the probability distribution function for the pair jand i to be at distance d, as is given by P_(jlc)(d/x). Hence,

$\begin{matrix}{d_{jl}^{c} = {\underset{d \in S}{\arg \; \max}\mspace{14mu} {P_{jlc}\left( {d/x} \right)}}} & (4)\end{matrix}$

The confidence score S_(jl) ^(c) associated with the observation of isP_(jlc)(d_(jl) ^(c)/x). Further, d_(i)=distance (loc_(j), loc_(l)), isthe distance between the locations of individual j (loc_(j)) andindividual i (loc_(l)).

In an embodiment of the present invention, the grouping module (210)groups two users having similar current location information at theparticular location with the predefined density criteria. The predefineddensity criteria may be derived employing a density based clusteringmethod.

In an embodiment of the present invention, the clustering algorithm isused to create groups. The clustering algorithm may follow the stepslike, the minimum group size is G_(min) and maximum group size isG_(max). The maximum distance of an individual from the centroid of thegroup is d_(icmax). The Density based clustering (DBSCAN) algorithm maybe used to form groups with density criteria ‘ε’ such that there has tobe N individual per unit area. The clustering algorithm gives the coregroups as clusters and individuals not belonging to any groups aretreated as outliers.

In an embodiment of the present invention, the fusion engine (212) isadapted for deriving the social interaction information of the user. Thefusion engine (212) is hosted by a back end server (120). The fusionengine is used to fuse the data or information received from the derivedaccurate straight line distance between the two users in a group at theparticular location and a web sensor (214).

In an embodiment of the present invention, the fusion engine (212) thedata from all these sensors as well as the web based soft-sensors arefed to the multimodal fusion module. Each sensor may have an errorprobability and also a confidence score with which it reports a reading.The fusion module reads data from multiple sensors along with mentionedproperties for each reading. The engine then infers commonality fromreporting of multiple sensors and come up with an aggregated attributefor the edge between the vertices under consideration. For example“proximity” reported by a set of sensors may be fused with audioanalysis to arrive at a conclusion such as “conversation”. Thisaugmented with a location of conference room can be used to deduce a“meeting” whereas the same will be deduced as “chat” if the locationchanges to the coffee machine. Another aspect of multimodal sensorfusion may be used for error reduction and cancelation. For example,where 3D Camera reports proximity between two people at a location witha moderate degree of confidence, however the location for one of thepersons does not match the location derived from accelerometer. In sucha case the 3D camera data may be rejected as a “false positive”.

In an embodiment, the web sensor (214) may be selected from the groupcomprising of social networking website profile of the user, emailheaders of the user, RSS feed and social blog profile of the user. Thesocial networking sites like Facebook, Twitter etc. provides access tovarious information's like profile data which is in the form ofstructured data. This structured data is gathered for separately parsingthe structured data to extract the interests of the individual. Theinterests may provide an important property for the edge of the socialgraph as two people having a common interest are likely to be connectedacross that. The structured data mining, unstructured data mining fromuser's blogs and social posts may be obtained for forming edgeattributes. The email headers for the person may be scanned tounderstand real life communications of the individual, which may provideinformation like To, CC and Subject fields giving an indication of theuser's communication with the other people.

In an embodiment of the present invention, the location information forthe j^(th) individual as loc_(j) is given in equation (2). Thecorresponding confidence value (S_(j) ¹) of the detected location is the(loc_(j)). This is done for all N individuals. We term the location for“j” obtained from equation (2) as loc_(j) ¹. The pair-wise distance iscomputed by “mobile proximity sensing” and “3D camera data” as d_(jl)^(m) and d_(jl) ^(c) for individual's j and i. The location informationof j as derived from “mobile proximity sensing” of j-l pair is spherecentered on loc_(l) ¹ with radius d_(jl) ^(m). Thus the probability ofthe location of j derived from the location of i and the distancebetween the j-i pair is given by P(loc_(jl) ^(m)/x)=P(loc_(l)¹)*P(d_(jl) ^(m)).

Combined probability of the location of j derived from all otherindividuals is given as,

$L_{j}^{m} = {\prod\limits_{{l = 1},{l \neq j}}^{N}\; {P\left( {{loc}_{jl}^{m}/x} \right)}}$

Finally, the location of j derived from “mobile proximity sensing” isgiven as,

$\begin{matrix}{{loc}_{j}^{m} = {\arg \; {\max\limits_{{loc}_{jl}^{m} \in S}\mspace{14mu} \left( L_{j}^{m} \right)}}} & (4)\end{matrix}$

The corresponding score is given as S_(j) ^(m)=L_(j) ^(m)(loc_(j) ^(m)).

The location of j as computed from “3D camera data” obtained from thedistance between j-l pair is given as loc_(jl) ^(c)

loc_(jl) ^(c)=loc_(lj) ¹ +d _(jl) ^(c)  (5)

The location of “j” obtained in equation (5) is through the location of“i”. Hence the score for obtaining equation (5) is S_(i) ¹*S_(jl) ^(c)

Thus the final fused location for j^(th) individual is obtained as theweighted sum obtained from different observations, where the weights arethe confidence scores of the individual observations is given by,

$\begin{matrix}{{{loc}_{j} = \frac{{S_{j}^{1}*{loc}_{j}^{1}} + {S_{j}^{m}*{loc}_{j}^{m}} + {\sum\limits_{{l = 1},{l \neq j}}^{N}\; {S_{l}^{1}*S_{jl}^{c}*\left( {{loc}_{l}^{1} + d_{jl}^{c}} \right)}}}{S_{j}^{1} + S_{j}^{m} + {\sum\limits_{{l = 1},{l \neq j}}^{N}{S_{l}^{1}*S_{jl}^{c}}}}},{{\forall j} = 1},2,{\ldots \mspace{14mu} N}} & (6)\end{matrix}$

Where, N is the number of individuals in the proximity.

FIG. 3 illustrates the logical flow (300), as shown in step (302) theinternal sensors (104) present in the mobile communication device (102)are activated for sensing information related to various activities doneby a user. In step (304) the processing module (110) checks for presenceof any external sensors and connects with them through a communicationmeans like USB, Wi-Fi, Bluetooth and the like. As shown in step (306),the processing module (110) checks for any user activity such asdownloading process or browsing occurring on the mobile communicationdevice (102) as shown in step (308) and if so the processor module (110)waits for a predefined time step (306) and again checks for useractivity. As presented in step (310) and (312) a check for any useractivity occurring on the processing module (110) instructs the internalsensors (104) and external sensors (118) to sense user activity andtransfer the captured data to the processing module (110). Theprocessing module (110) then analyzes this data, separates contextinformation and transmits only relevant information to the transmissionmodule (114) which further transfers this information to the backendserver (120) as shown in step (314). Specific to the sensors underconsideration, the processing module (110) performs the followingactivities—accelerometer analytics for activity detection, basebandcommunication using ultrasound for localization and proximity detection,microphone captured audio analytics for emotion detection andneighborhood sensing, camera for ambient lighting detection and analysisof external physiological sensors such as EEG sensor for furtherinsights into user context. At step (316) the back end server (120) andits underlying fusion and decision module performs analysis to identifyuser personal context and store this information in the data base (122).At step (318) this stored information is used by the front endapplications (124) for creating individual and aggregated user contextthat can be used for Organizational Behavior Analysis, WorkspaceErgonomics Analysis, Discovering the user's physical interaction networkand also for Measuring and analyzing user response in User Studies etc.

Referring to FIG. 4, the logical flow of the personal context basedsocial interaction deriving method (400) is shown. As shown in step(402) the process starts by locating the user within predefined range atthe particular location. At step (404) personal context of the user isidentified using external sensors (118) and internal sensors (104)embedded in the mobile communication device (102). At the step (406) theidentified personal context of the user is assigned a predefinedconfidence value. At the step (408) the precise current locationinformation of the user is obtained within the particular location. Atthe step (410) the accurate straight line distance between at least twousers at the current location is derived. At the step (412) the twousers having similar current location information at the particularlocation are grouped together with the predefined density criteria. Theprocess ends at step (414) wherein the social interaction information ofthe user in a group at the particular location is derived.

Referring to FIG. 5 as an exemplary embodiment of the present invention,depicts the normalized histogram representing number of phones beingdetected at the same location in which, a microphone sensor “i” which isstatic and its location is known, is considered. At a particularlocation loc_(k), a mobile phone (j) may be used to play a sound at aknown volume. The fixed microphone “i” receives the sound and computes adistance “d_(kj)”. The distance may be computed with the principle thatat the receiver using microphone sensors and the like, the attenuationof the volume of the sound is inversely proportional to the distancefrom the sound source. This computation is performed for N such phones.A histogram is computed on number of phones being detected at the samelocation. A sample normalized histogram plot is shown in FIG. 5. It isclear from the FIG. 5, that maximum numbers of phones are detected at adistance 11 units, this is the actual distance d_(k). But, there may becertain non-zero phones whose distance is detected as different fromd_(k). This may be due to the error in observation, difference in phonemodels, and environmental effects. This process is repeated for all theloc_(k) or equivalently d_(k). Every time when the receiver observes adistance “d_(kj)”, there is a confidence score “S_(jk)” associated withthe observation. The function shown in FIG. 5 gives S_(jk) to derive theprobability distribution P_(jl)(x/loc_(k)) of the observed value given adistance d_(k). During the actual detection process, based on thereceived sound from the mobile phone, the probability of the locationloc_(k) observed by i^(th) sensor is P_(jl)(loc_(k)/x). This is obtainedusing Bayes equation.

What is claimed is:
 1. A method for identifying personal context of at least one user having a portable mobile communication device at a location for deriving social interaction information of the user, the method comprising: determining a location of at least one user within a predefined range by identifying a personal context of the user at the location, wherein the personal context of the user is identified using at least an external sensor and an internal sensor embedded in the mobile communication device; assigning predefined confidence values to the identified personal context; obtaining current location information relating to the user at the location by fusing the assigned confidence values; grouping, based on predefined density criteria, at least two users having similar current location information at the location of the user; estimating, using the external sensor and internal sensor, a straight line distance between the at least two grouped users; and deriving social interaction information of the user by fusing the current location information of the user, the estimated straight line distance, and data received from a web sensor.
 2. The method of claim 1, wherein the personal context of the user is at least one of an identity, proximity, an activity, and a location.
 3. The method of claim 1, wherein the mobile communication device is at least one of a mobile phone, a smartphone, a laptop, a palmtop, and a personal data assistant (PDA).
 4. The method of claim 1, wherein the external sensor is at least one of a building management system (BMS), a 3-D depth camera, and an ultrasound sensor.
 5. The method of claim 1, wherein the internal sensor is at least one of an accelerometer, a magnetometer, a speech recognizer, a Bluetooth RSSI, and a wireless frequency (Wi-Fi) signature.
 6. The method of claim 1, wherein the predefined density criteria is derived using a density based clustering method.
 7. The method of claim 1, wherein the external sensor and the internal sensor are physical sensors.
 8. The method of claim 1, wherein the web sensor is at least one of a social networking website profile of the user, email headers associated with the user, an RSS feed, and a social blog profile of the user.
 9. The method of claim 1, wherein: the external sensor and the internal sensor are communicatively coupled with a processing module; the external sensor transmits sensory data to the processing module via an external sensor interface; and the internal sensor transmits sensory data to the processing module directly.
 10. The method of claim 9, wherein the sensory data transmitted by the external sensor and the internal sensor is stored in an internal memory communicatively coupled with the processing module.
 11. The method of claim 1, wherein the derived social interaction information is displayed using a front end application.
 12. The method of claim 1, wherein the derived social interaction information is at least one of an interaction while working in cubicle, an interaction while leading a meeting, an interaction of a presenter in a session, a passive listener in meeting, and an interaction during a group discussion.
 13. The method of claim 1, wherein the fused confidence value, the fused current location information, and the personal context of the user are stored in a database that is communicatively coupled to a back end server.
 14. The method of claim 1, further comprising switching between other mobile device applications of the internal sensor and personal context of the user when an interrupt is generated from regular activities of the mobile communication device.
 15. A system for identifying a personal context of at least one user at a location for deriving social interaction information of the user, the system comprising: a mobile communication device, comprising: an internal sensor adapted for sensing a personal context of the user and sending the personal context to a processing module of the mobile communication device; the processing module adapted to perform on-board processing of sensory data received from an external sensor and the internal sensor; an internal memory communicatively coupled with the processing module and adapted to store the processed sensory data; a transmission module adapted to transmit the processed sensory data to a back end server hosting a fusion engine and communicatively coupled to a database adapted to store fused confidence values, fused current location information of the user, and the personal context of the user; and a switching module adapted for switching between other mobile device applications of the internal sensor and the personal context of the user when an interrupt is generated from regular activities of the mobile communication device; and wherein the external sensor is adapted for sensing the personal context of the user and sending the personal context of the user to the processing module via an external sensor interface of the mobile communication device, and wherein the back end server is adapted for fusing assigned predefined confidence values of the personal context of the user, current location information of the user, and a derived accurate straight line distance between at least two users in a group at a location of the user.
 16. The system of claim 15, wherein the system further comprises: a localization module adapted for locating at least one user within a predefined range by identifying the personal context of the user at the location, wherein the personal context of the user is identified using the external sensor and the internal sensor; a confidence value assignment module adapted for assigning the predefined confidence values to the identified personal context of the user; a current location identification module adapted for obtaining current location information of the user within the location by fusing the assigned confidence values using a fusion engine; a grouping module adapted for grouping, using a predefined density criteria, at least two users having similar current location information at the location; and a proximity estimation module adapted for estimating, using the external sensor and the internal sensor, an accurate straight line distance between the grouped at least two users; wherein the fusion engine is adapted for deriving the social interaction information of the user by fusing the current location information of the user and estimated accurate straight line distance.
 17. The system of claim 15, wherein the mobile communication device is at least one of a mobile phone, a smartphone, a laptop, a palmtop, and a personal data assistant (PDA).
 18. The system of claim 15, wherein the external sensor is at least one of a building management system (BMS), a 3-D depth camera, a wearable EEG, and an ultrasound sensor.
 19. The system of claim 15, wherein the internal sensor is at least one of an accelerometer, a magnetometer, a speech recognizer, and a Wi-Fi signature.
 20. A non-transitory computer readable medium storing machine readable instructions executable by one or more processors for: determining a location of at least one user within a predefined range by identifying a personal context of the user at the location, wherein the personal context of the user is identified using at least an external sensor and an internal sensor embedded in the mobile communication device; assigning predefined confidence values to the identified personal context; obtaining current location information relating to the user at the location by fusing the assigned confidence values; grouping, based on predefined density criteria, at least two users having similar current location information at the location of the user; estimating, using the external sensor and internal sensor, a straight line distance between the at least two grouped users; and deriving social interaction information of the user by fusing the current location information of the user, the estimated straight line distance, and data received from a web sensor. 